What is High Performance Data Analytics?

High Performance Data Analytics (HPDA) refers to the use of High Performance Computing (HPC) to analyze large data sets for patterns and insights.

High performance data analytics definition

High Performance Data Analytics unites HPC with data analytics. The process leverages HPC’s use of parallel processing to run powerful analytic software at speeds higher than a teraflop or (a trillion floating-point operations per second). Through this approach, it is possible to quickly examine large data sets, drawing conclusions about the information they contain.

Why high performance data analytics?

Some analytics workloads do better with HPC rather than standard compute infrastructure. While some “big data” tasks are intended to be executed on commodity hardware in a “scale out” architecture, there are certain situations where ultra-fast, high-capacity HPC “scale up” approaches are preferred. This is the domain of HPDA. Drivers include a sensitive timeframe for analysis, e.g. real time, high-frequency stock trading or highly complex analytics problems found in scientific research.

HPE high performance data analytics

HPE offers data scientists the world’s most powerful, most efficient HPC solutions for HPDA workloads. Our HPC solutions are renowned for powering analytics at any scale with purpose-built technologies.

Using a data lake to improve data storage, integration, and accessibility

As data continues to expand and organisations strive to extract value from it, data lakes will become a key way to unlock new systems of insight, intelligently manage data, and achieve competitive advantage.

"In the fast-paced world of high-frequency trading (HFT), financial services organisations rely heavily on technology to increase the speed of operations. For today’s traders, simply executing an order as quickly as possible is no longer enough.